Even so, our course “Machine learning and applications in Science and Industry” was the most popular.
Focus of the course (heavily influenced by time constraints: only 4 days)
was to give a wide overview of useful models in Machine Learning and their applications in very different areas,
and even contained optional practice!

Also we tried to create a nice bridge between models and their real-life applications.
Many of the examples were from particle physics — an area that we’re working in
(tracking, tagging, reweighting, uniform boosting, particle identification, simulation refinement,
tuning of simulation parameters, etc.).
However we also included some notable examples from other data-intensive areas: astronomy, neuroscience, medicine, climatology and biology.

In the second day we made focus on tree-based techniques, specially boosting, that aren’t popular in research now,
but work very well in practice and are best-performers in many examples with tabular data

decision trees for classification and regression

Random Forest

AdaBoost and Reweighter

Gradient Boosting for classification, regression and ranking (ordering of items)